Device and method for recognizing hand gestures using time-of-flight sensing

ABSTRACT

An electronic device includes at least one laser source configured to direct laser radiation toward a user&#39;s hand. Laser detectors are configured to receive reflected laser radiation from the user&#39;s hand. A controller is coupled to the at least one laser source and laser detectors and configured to determine a set of distance values to the user&#39;s hand for each respective laser detector and based upon a time-of-flight of the laser radiation. The controller also determines a hand gesture from among a plurality of possible hand gestures based upon the sets of distance values using Bayesian probabilities.

TECHNICAL FIELD

The present disclosure relates to devices that determine hand gestures,and more particularly, to electronic devices that determine handgestures using laser sources and laser detectors.

BACKGROUND

Mobile wireless communications devices, tablets, and similar deviceshave touch screens that often are equipped with proximity detectors,such as infrared sensors, that detect simple gestures. For example, thedevices may detect the approach or movement of an object, such as afinger or mechanical stylus. This detection may be used to disable atouch screen function for the mobile wireless communications deviceduring a call when the device is near the ear of a user. Infraredsensors may use the brightness reflected by the target object todetermine a rough estimate of the distance to the moving object.

Other more complicated gesture recognition systems interpret simple handgestures to enable touchless gesture control of wireless communicationsdevices, tablets and similar devices. The device may respond to simple,touchless commands, distinguishing between more complicated simple handgestures. These systems allow intuitive ways for users to interact withtheir electronic devices. For example, a hand gesture, such as a handwipe, may instruct the device to implement a page turn for a bookapplication on a tablet. These current hand gesture recognition systems,however, involve intensive processing of data using complicatedalgorithms, often including time-of-flight and machine learning andbased algorithms that require extensive computations to discriminatebetween even the most common hand gestures, such as a single tap orsingle wipe. More efficient hand gesture recognition systems are desiredto facilitate their use with smaller and more compact electronicdevices, such as cell phones and tablets but also consumer electronicdevices, such as light dimmers and water faucets, without usingexcessive processing resources and memory.

SUMMARY

This summary is provided to introduce a selection of concepts that arefurther described below in the detailed description. This summary is notintended to identify key or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in limiting the scope ofthe claimed subject matter.

An electronic device may comprise at least one laser source configuredto direct laser radiation toward a user's hand. A plurality of laserdetectors are configured to receive reflected laser radiation from theuser's hand. A controller is coupled to the at least one laser sourceand plurality of laser detectors and configured to determine a set ofdistance values to the user's hand for each respective laser detectorbased upon a time-of-flight of the laser radiation, and determine a handgesture from among a plurality of possible hand gestures based upon thesets of distance values using Bayesian probabilities.

The controller may be configured to derive the Bayesian probabilities asa confusion matrix for each of the laser detectors. The controller maybe configured to weight the sets of distance values using the Bayesianprobabilities to determine the hand gesture. A memory may be coupled tothe controller to store the sets of distance values. The controller maybe configured to determine the hand gesture as at least one of a singletap, a double tap, a page flip, a single wipe, a double wipe, and arotation.

The laser detector may comprise a single photon avalanche diode (SPAD)detector. The SPAD detector may comprise an array of single photonavalanche diodes. The laser source, SPAD detector, and controller may beformed as a single integrated circuit (IC). The laser source maycomprise a vertical-cavity surface-emitting laser (VCSEL). The lasersource may comprise an infrared (IR) laser source.

A method of determining a hand gesture comprises directing laserradiation from at least one laser source toward a user's hand andreceiving with a plurality of laser detectors reflected laser radiationfrom the user's hand. The method further comprises a controller coupledto the at least one laser source and plurality of laser detectors todetermine a set of distance values to the user's hand for eachrespective laser detector based upon a time-of-flight of the laserradiation, and determine a hand gesture from among a plurality ofpossible hand gestures based upon the sets of distance values usingBayesian probabilities.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects, features and advantages will become apparent from thedetailed description of which follows, when considered in light of theaccompanying drawings in which:

FIG. 1 is a fragmentary, partial perspective view of the electronicdevice for determining hand gestures in accordance with a non-limitingexample.

FIG. 2 is another perspective view of the electronic device as shown inFIG. 1 in accordance with a non-limiting example.

FIGS. 3A and 3B are tables showing different hand gestures that can bedetermined from the electronic device in accordance with a non-limitingexample.

FIG. 4 is a high-level flowchart showing a process for operating theelectronic device in accordance with a non-limiting example.

FIG. 5 is a block diagram of basic components of the electronic devicein accordance with a non-limiting example.

FIG. 6 is a more detailed block diagram of the electronic device asshown in FIG. 5 in accordance with a non-limiting example.

FIG. 7 is an example of MAD values for wipe and tap hand gestures inaccordance with a non-limiting example.

FIG. 8 is a graph showing cumulative distribution function versus MADvalues for a single tap in accordance with a non-limiting example.

FIG. 9 is a graph similar to FIG. 8, but showing values for a singlewipe as left to right in accordance with a non-limiting example.

FIG. 10 is a graph similar to FIG. 8, but showing values for a singlewipe as right to left in accordance with a non-limiting example.

FIG. 11 is a graph similar to FIG. 8, but showing values for a singlewipe as bottom to top in accordance with a non-limiting example.

FIG. 12 is a graph showing the combined cumulative distribution functionversus the MAD values for the single tap and the single wipes from FIGS.8-11 in accordance with a non-limiting example.

FIG. 13 is a graph showing the calculated threshold line based upon theprevious graphs of FIGS. 8-12 in accordance with a non-limiting example.

FIG. 14 is a table showing a statistical discrimination with triggeredtime differences in accordance with a non-limiting example.

FIG. 15 is a table showing examples of the scoring probability (akaconfusion matrix) with the single tap and single wipe in accordance witha non-limiting example.

FIG. 16 is an example of a laser detector positional diagram andanalytical analysis of a single wipe movement in accordance with anon-limiting example.

FIGS. 17-19 are graphs and computations for angular decision criterionexamples for a first triggered sensor in accordance with non-limitingexamples.

DETAILED DESCRIPTION

Different embodiments will now be described more fully hereinafter withreference to the accompanying drawings, in which preferred embodimentsare shown. Many different forms can be set forth and describedembodiments should not be construed as limited to the embodiments setforth herein. Rather, these embodiments are provided so that thisdisclosure will be thorough and complete, and will fully convey thescope to those skilled in the art.

The electronic device as disclosed may operate as a sensor device andpermits hand gesture recognition using time-of-flight detection ofsensor signals in low cost, low power devices, such as a mobile wirelesscommunications device or consumer electronic devices, such as lightdimmers and water faucets, while using less processing power and memoryfootprint.

Referring now to FIG. 1, there is illustrated the electronic device 30as a sensing device, in accordance with a non-limiting example, such aspart of a mobile wireless communications device 31, for example, a cellphone, tablet, or similar device. A fragmentary view of the interior ofthe mobile wireless communications device 31 is illustrated to show inblock format basic components of the device 30 used for determining handgestures, in accordance with a non-limiting example. A laser source 32is configured to direct laser radiation towards a user's hand (H) asillustrated. At least one laser detector 34 in this example isconfigured to receive the reflected laser radiation from the user's hand(H). A controller 36 interoperates with a memory 38 and is coupled tothe laser source 32 and laser detector 34 and configured to determine aplurality of sets of distance values to the user's hand based upon atime-of-flight of the laser radiation. The controller 36, in oneexample, calculates a Mean Absolute Deviation (MAD) value during acertain duration, based upon the plurality of distance values andidentifies whether the user's hand is moving in a first or secondgesture based upon the MAD value. In many devices, such as a mobilewireless communications device 31, the controller 36 is a microprocessoror a microcontroller with multiple functions or could be separate andfunction as a specific sensor processor. The first or second gesture mayinclude a tap or wipe, and in the illustrated example, FIG. 1 shows theuser sliding their hand (H) as a wipe.

FIG. 2 shows the user's hand in a tap motion, and in this example,moving her index finger as a tap towards or onto the display screen 40of the mobile wireless communications device 31. A calculated MAD valueabove a threshold indicates a tap, and a calculated MAD value under thethreshold indicates a wipe. For example, the threshold may be based uponthe MAD value for distance values that are closer to each other, such asduring a wipe, as compared to the distance values that are changingrapidly during a tap when the finger or hand moves from top to bottomand then back to top. With a wipe, on the other hand, the distancevalues are more constant and closer to a constant height above thescreen 40 as the user moves their finger or hand laterally across thescreen in that “wiping” motion. As will be readily understood by thoseskilled in the art, the term “hand gesture” will be used herein forsimplicity to indicate movement of the user's entire hand, or just aportion thereof, such as a finger.

It is possible to use a plurality of laser detectors 34 to determine agreater range of hand gestures. More than one laser source 32 may alsobe used. On a basic level with a plurality of laser detectors 34, thecontroller 36 is configured to calculate a MAD value for each of theplurality of distance values during a certain duration for each laserdetector 34 and calculate an average MAD value to identify whether theuser's hand is moving in the first or second gesture such as a wipe ortap. If a plurality of laser detectors 34 receives reflected laserradiation from the user's hand, the controller 36 determines the set ofdistance values to the user's hand for each respective laser detector34. Based upon the time-of-flight of the laser radiation for eachdetector 34, it is possible to determine a hand gesture from among aplurality of possible hand gestures based upon the sets of distancevalues using Bayesian probabilities. The controller 36 may deriveBayesian probabilities, including a confusion matrix, for each of thelaser detectors and weight the set of distance values using the Bayesianprobabilities to determine a hand gesture as explained in greater detailbelow. The memory 38 may store the distance values for furtherprocessing, comparison and probability calculations. A greater variationof hand gestures may be determined, such as at least one of a singletap, a double tap, a page flip, a single wipe, a double wipe, and arotation. A single laser source 32 and single laser detector 34 may beused to distinguish between a tap and a wipe. Distinguishing betweenmore complex hand gestures will normally require the electronic device30 to include at least a plurality of laser detectors 34 for Bayesianprobabilities calculations.

A non-limiting example of different gestures that can be determinedusing the electronic device 30 are shown in FIGS. 3A and 3B. Fourteen(14) different hand gestures numbering from “00” to “13” are illustratedwith their hand gesture names and a brief description of each handgesture. The acronym LtR refers to left to right and RtL refers to rightto left. LtRtL refers to left to right and then back to left, such as adouble wipe with the user's hand in a natural flat position. RtLtRrefers to right to left then back to right, such as the user's hand in anatural flat position. Other hand gesture movements could be from bottomto top (BtT) in a front to rear motion, or top to bottom (TtB), bottomto top then back to bottom (BtTtB), or top to bottom then back to top(TtBtT). A hand gesture can also be the hand's rotation clockwise orcounterclockwise as shown in gesture nos. 12 and 13. The page flipgesture is a hand rotation either clockwise or counterclockwise. Whendetected by the controller 36, it will turn the virtual page on thedisplay screen 40 in a book reading application, for example.

The controller 36 may determine other single-touch and multi-touchgestures, including a pan, flick, touch and hold, and pinch and stretchas non-limiting examples. Pinch and stretch may occur when two fingersare pointed down within the bounded area of the display screen 40followed by the fingers moving closer together as a pinch or furtherapart as a stretch to reduce or enlarge a specific area displayed on thescreen 40.

A flowchart showing a high-level sequence of steps for operation of theelectronic device, in accordance with a non-limiting example, is shownin FIG. 4. As illustrated, the process start at Block 100. Laserradiation is directed toward a user's hand (Block 102). At least onelaser detector receives the reflected laser radiation (Block 104). Thecontroller determines distance values (Block 106). The controller alsodetermines if multiple detectors are used (Block 108), and if not andonly a single detector is used, then the controller determines whetherthere is a tap or wipe as a simple hand gesture (Block 110). The processends at Block 111. If multiple detectors are used, then a tap or wipecan be determined, but also more complex hand gestures may bedetermined, such as a page flip (Block 112). The process ends (Block114).

As shown in a fragmentary and high-level block diagram of FIG. 5, theelectronic device 30 is formed as a sensing device and the laser source32, laser detector 34 and controller 36 are a single integrated circuit(IC) in this example. The laser source 32 in this example is an infrared(IR) laser source configured to direct laser radiation towards theuser's hand. The laser detector 34 is configured to receive thereflected laser radiation from the user's hand, and in this example, isformed as a Single Photon Avalanche Diode (SPAD) detector 34 thatreceives the reflected laser radiation. In an example, the detector 34is an array of single photon avalanche diodes. The laser source 32 maybe formed as a Vertical Cavity Surface-Emitting Laser (VCSEL). Multiplelaser sources 32 and laser detectors 34 may also be used andincorporated with one IC with multiple or “n” arrays of SPAD detectorsor as separate, multiple devices 30. It is possible that only one lasersource 32 may be used and a plurality of laser detectors 34 spaced fromeach other and connected to the controller 36 or a plurality of lasersources 32 as shown in FIG. 5.

As noted before, when a plurality of laser detectors 34 are used, it ispossible to determine a hand gesture from among a plurality of possiblehand gestures as shown in FIGS. 3A and 3B based upon the sets ofdistance values using the Bayesian probabilities and including aconfusion matrix for each of the laser detectors as will be explained ingreater detail below.

FIG. 6 is a more detailed block diagram of the electronic device 30 as asensing device and showing details of the IR laser source 32 formed asan array of vertical-cavity surface-emitting laser (VCSEL) elements 50.Each VCSEL element 50 is formed as a semiconductor laser diode thatemits light as laser radiation perpendicular to the surface and includesactive layers with the thickness of a few nanometers (nm). The VCSELelements 50 can be formed from different semiconductor processingtechniques and include different active layers.

Most VCSEL elements share a general configuration. Electrical carriersare converted into light above and below an active layer. There may bemultiple layers of alternating refractive index resonant mirrors havinghigh reflectivity to achieve gain. The resonant mirrors are formed inone example as doped semiconductor mirrors that provide electricalcontacts to active layers that may be defined by the width of anoxidized layer near an active layer. They may be formed in a singleepitaxial growth process where semiconductor wafer processing stepsdefine the emission area and provide electrical terminals to individuallaser-diodes forming the VCSEL element 50. Each VCSEL element 50 is avertical structure and a large number of VCSEL elements as laser diodesmay be placed next to each other as a two-dimensional array andconnected individually or in parallel.

The VCSEL array may be formed from thousands of the smaller VCSELelements 50 and manufactured on GaAs wafers where the pitch betweenindividual elements is about 40 um in a non-limiting example. In theexample used with reference to the electronic device of FIG. 1,different laser wavelengths may be used. In an example, the laser source32 has an operating wavelength in the range of 800-900 nanometers for IRuse, but can extend up to 1,300 nm or higher depending on applications.Depending on other applications, lower wavelengths may be used.

A single and simplified example of a VCSEL element 50 is illustrated andincludes a metal contact 52 with an opening 54 through which the laserradiation is emitted. The VCSEL element 50 includes an upper Braggreflector 56 formed of P-type material, a quantum well 58 and a lowerBragg reflector 60 formed of an N-type material. An N-substrate 62 andmetal contact 64 are included. The upper and lower Bragg reflectors(DBR) 56, 60 form mirrors and are parallel to a wafer surface and haveone or more quantum wells for laser light generation. Usually the Braggreflector mirrors include alternating high and low refractive indicesand a thickness of about a quarter of the laser wavelength in anon-limiting example to yield a high reflectivity. These mirrors balancethe short axial length of the gain region. The upper and lower Braggreflector mirrors may be formed of P-type and N-type material to form adiode junction, but other N-type and P-type regions may be embeddedbetween mirrors in non-limiting examples. The GaAs substrate allowsmultiple epitaxial layers to be grown. A microprocessor 66 is connectedto each VCSEL element 50 to provide current control and any type oflaser aiming and coordination among the VSCEL elements 50.

FIG. 6 further illustrates an array of single photon avalanche diodes(SPAD's) that form an “n” SPAD array with each diode forming a laserdetector element 70 connected in this example to a microprocessor 72.Each SPAD laser detector element 70 is a solid-state photo detector inwhich a photo-generated carrier can trigger an avalanche current todetect low intensity signals such as a single photon. The microprocessor72 processes the signal arrival times of photons with a jitter of a fewtens of picoseconds. Usually a SPAD laser detector element 70 includes areverse bias P-N junction to detect the laser radiation such as infraredradiation, for example, and operates with reverse-bias voltage above thebreakdown voltages in a “Geiger mode” similar to a conventional Geigercounter. Different SPAD laser detection elements 70 may be used as knownto those skilled in the art depending on the environmental to which theelectronic device is employed to determine hand gestures. Variousquenching circuits may be used, including passive and active quenching.Examples are disclosed in U.S. Pat. No. 8,610,043 to Baxter and U.S.Patent Publication No. 2014/0124652, the disclosures which are herebyincorporated by reference in their entirety.

The controller 36 operates with the microprocessors 66, 72 and variousVCSEL elements 50 and SPAD laser detector elements 70 to determine a setof distance values to a user's hand for each respective laser detectoras a SPAD laser detector element using time-of-flight of the laserradiation. In an example as noted before, the controller 36 determines ahand gesture by calculating a Mean Absolute Deviation (MAD) value basedupon the plurality of distance values and determines whether the user'shand is moving in a first or second gesture based upon the MAD valuesuch as a tap or wipe. Also, with a plurality of detectors as describedbefore, a more complex hand gesture may be determined from among aplurality of possible hand gestures based upon the sets of distancevalues using Bayesian probabilities.

Because time-of-flight measurements are processed, the device 30 as asensing device is more precise than other gesture detection systems andmay detect gestures up to about 50 cm away from a mobile wirelesscommunications device or other device incorporating the sensing device30, such as a tablet, notebook computer, consumer electronic devices orother device containing the laser source 32 and laser detector 34.Usually, the mobile wireless communications device, tablet or otherdevice may include small apertures to emit the laser radiation andpermit its return to the laser detector. Because the laser radiation isnarrow, the apertures can be very small and concealed, such as behind aspeaker grill.

In one example of the electronic device 30, the controller 36 calculatesa Mean Absolute Deviation (MAD) value based on the plurality of distancevalues, measured during a certain duration. The Mean Absolute Deviationmay be around the “mean” and is referred also as the mean deviation orthe average absolute deviation. This value is used instead of standarddeviation as a more simple measure of variability than standarddeviation.

It is also possible to use Bayesian probabilities to determine a handgesture from among a plurality of possible hand gestures based upon thesets of distance values when a plurality of detectors are used. In aBayesian probability calculation, prior probabilities such as stored inmemory 38 and the processed data are updated in light of new relevantdata or evidence, for example, changing distance values as measured bythe different laser detectors 34. Usually random variables as unknownquantities are used to model sources of uncertainty in statisticalmodels. A prior probability distribution takes into account theavailable prior data and information regarding distance calculations andprobable hand gesture determinations. For example, different distancevalues may be weighted when used to determine the confusion matrix foreach of the laser detectors. Previous values may be stored in memory 38and then compared. When data becomes available, a posteriordistributions may be calculated using Bayes' formula. In an exampledevice 30, the controller 36 is configured to derive the Bayesianprobabilities as a confusion matrix for each of the laser detectors. Inthis example, a confusion matrix contains information about actual andpredicted classifications such as the different hand gestures. Differenthand gestures may be classified using data in a matrix that is stored inthe memory 38. For example, a wipe movement could be one classificationand a hand flip another classification, and comparisons can be made as aportion of the total number of predictions that were correct. A truepositive rate is the proportion of positive cases that were correctlyidentified. A false positive rate is the proportion of negative casesthat were incorrectly classified as positive. A true negative rate isthe proportion of negative cases that were classified correctly. A falsenegative rate is the proportion of positive cases that were incorrectlyclassified. The precision may be based on the proportion of predictedpositive cases that were correct.

There now follows a description of how the controller 36 determines aplurality of distance values to the user's hand using a single laserdetector or a plurality of laser detectors 34. Based upon time-of-flightof the laser radiation, the controller 36 determines a hand gesture fromamong a plurality of possible hand gestures based upon the sets ofdistance values using the Bayesian probabilities, or calculates a MADvalue and identifies whether the user's hand is moving in a first orsecond gesture such as a tap or wipe based upon the MAD value. For asimplified single tap and single wipe detection, for example, includinguse of only one laser detector, distance values may be changing during atap such as moving the hand or finger from top to bottom and then totop, while distance values are more constant during a wipe, such aswiping the finger or hand across the surface of the mobile wirelesscommunications device 31 or other device at a more constant distancefrom the display area 40 of the device.

The table in FIG. 7 are examples of MAD values for five different handgestures as illustrated as the four types of wipe as left to right(LtR), right to left (RtL), bottom to top (BtT), and top to bottom(TtB), and a single tap. In this example, four laser detectors 34 areused. The MAD values for each detector are listed in this example, andeach corresponds to the left, top, bottom, and right laser detectors 34when oriented in a diamond or square configuration. It should beunderstood that one detector 34 may be used, however, if only a wipe ortap is to be determined. An example measurement determination of athreshold of around 1.2 to 1.3 to allow the best discrimination is shownin the examples of FIGS. 8-12, showing cumulative distribution functionversus the MAD value for a single tap (FIG. 8), a single wipe as a leftto right (FIG. 9), a single wipe as right to left (FIG. 10), a singlewipe as bottom to top (FIG. 11), and a single tap versus a single wipe(FIG. 12). In FIG. 12, the left circle shows the single wipe and theright circle shows the single tap. The threshold determination of1.2/1.3 is shown in FIG. 13. This threshold may also be precalculated oradaptive and based on the multiple sensing through learning.

In an example using multiple laser detectors 34, the mean of distancevalues (over a non-idle period) per detector is calculated and the MADvalue is calculated. The number of detectors for which the MAD value islower (resp. higher) is counted as compared to the threshold T=1.2.

${NbGTT} = {{\sum\limits_{{sensor} = 1}^{4}{\left( {{MAD} \geq T} \right)\mspace{34mu}{NbLTT}}} = {\sum\limits_{{sensor} = 1}^{4}\left( {{MAD} \leq T} \right)}}$

It is a single tap if: NbGTT≧NbLTT. Other possibilities are that alllaser detectors 34 are triggered for a start and end of gesture or atthe same or close to the same instants for a tap. Another possibility isthe detectors 34 are triggered at different instants for a wipe.

It is also possible to use triggered time differences among the multipledetectors 34. For example, detectors 34 may be triggered by the startand end of a gesture or at the same or close to the same instants for atap, or triggered at different instants for a wipe. In this example,each detector 34 determines:

a) First non-idle instant: “Start Instant” SI;

b) Last non-idle instant: “End Time” EI; and

c) Arithmetic mean of “Start” and “End” Instants: “Middle Instant” MI.

The controller 36 may compute horizontal and vertical differenceinstants:

a) ΔH_Start=SI (Right Sensor)−SI (Left Sensor);

b) ΔH_End=EI (Right Sensor)−EI (Left Sensor);

c) ΔH_Middle=MI (Right Sensor)−MI (Left Sensor);

d) ΔV_Start=SI (Top Sensor)−SI (Bottom Sensor);

e) ΔV_End=EI (Top Sensor)−EI (Bottom Sensor); and

f) ΔV_Middle=MI (Top Sensor)−MI (Bottom Sensor).

An example of a statistical discrimination is shown in the table of FIG.14. The average value is in milliseconds and the middle differenceinstants are more reliable by the averaging of the jitter effect. Theabsolute values can help discriminate meaningful information from timejitter. For example, below 25 ms can be considered as a null ornegligible movement, and above 35 ms can be considered as an intentionalmovement.

A single tap may be detected as both negligible horizontal and verticaldifference instants. Single wipes may be detected as having a meaningfulmaximum difference instant (either H or V).

It is possible to combine the MAD and difference instant systems asfollows:

1) Compute mean and MAD of distance values (over non-idle period) persensor;

2) Count the number of detectors as sensors for which the MAD value islower (resp. higher) than the threshold T=1.3

${NbGTT} = {{\sum\limits_{{sensor} = 1}^{4}{\left( {{MAD} \geq T} \right)\mspace{34mu}{NbLTT}}} = {\sum\limits_{{sensor} = 1}^{4}\left( {{MAD} \leq T} \right)}}$

3) Compute ΔH_Middle and ΔV_Middle;

4) Compute scores for single tap and single wipes as follows:

${SingleTapScore} = {{NbGTT} + {\sum\limits_{{Direction} = {({H,V})}}\left( {{\Delta_{Middle}} \leq 15} \right)}}$${{SingleWipeScore} = {{NbLTT} + {2*\left( {{\max\limits_{{Direction} = {({H,V})}}{\Delta_{Middle}}} \geq 35} \right)}}},$and

5) Compare both scores (or derive a soft output).

An example maximum score is shown in the table in FIG. 15 for a singletap and single wipe with different examples of wipes as Left to Right(LtR), Right to Left (RtL), Back to Top (BtT), and Top to Bottom (TtB).

FIG. 16 is an example of a laser detector positional diagram showing theleft, right, top and bottom laser detector 34 positions and ananalytical analysis of a single wipe movement. In this example, the hand(H) is modeled as an infinite linear edge moving at a constant heightand constant speed V. The α (alpha) normal angle is with respect to theX-axis. The laser detector or sensor layout is modeled as a square witha half-diagonal “d” as illustrated, which depends on the upper hand'sheight of the upper hand. The measurements are taking into account thetime-of-arrival of first non-null measures per detector 34. An intuitivedecision may be made as to which is the first triggered detector 34based on a first time-of-arrival signal as a start instant. it ispossible to use a stop instant or a middle instant. It is also possibleto use a last triggered detector based on a last time-of-arrival as astart instant, stop instant, or middle instant. Angular decisioncriterion examples are shown in FIGS. 17, 18 and 19. Each has a firsttriggered left sensor 34 and shows computation of the differences oftime.

With these examples, it is possible to proceed with controller 36calculations as follows with the 0≦α≦π/4 application. The firsttriggered sensor is the left sensor. The controller 36 calculates:

${\partial t_{V}} = {{t_{T} - t_{B}} = {{\frac{2d\mspace{14mu}{\sin(\alpha)}}{v}\mspace{31mu}{\partial t_{H}}} = {{t_{R} - t_{L}} = \frac{2d\mspace{14mu}{\cos(\alpha)}}{v}}}}$

From above, the system can eliminate d and v:

$\frac{2d}{v} = \sqrt{\left( {\partial t_{V}} \right)^{2} + \left( {\partial t_{H}} \right)^{2}}$$\left\{ \begin{matrix}{{\cos\mspace{14mu}\alpha} = \frac{\partial t_{H}}{\sqrt{\left( {\partial t_{V}} \right)^{2} + \left( {\partial t_{H}} \right)^{2}}}} \\{{\sin\mspace{14mu}\alpha} = \frac{\partial t_{V}}{\sqrt{\left( {\partial t_{V}} \right)^{2} + \left( {\partial t_{H}} \right)^{2}}}} \\{{\tan\mspace{14mu}\alpha} = \frac{\partial t_{V}}{\partial t_{H}}}\end{matrix} \right.$Again for, 0≦α≦π/4 application, the first triggered sensor is the left.The last equation leads to:

${\tan\mspace{14mu}\alpha} = \frac{\partial t_{V}}{\partial t_{H}}$=>A can be estimated from instant differences.

The following benefits occur when operating the device 30 in thismanner:

a) there is no need for knowing d nor v;

b) there is an optimal detector for single wipe movements and handangle;

c) there is no need for complex arithmetic (only onedivision+LUT+logic);

d) it is easily extendable to the whole quadrant cases; and

e) it is easily extendable (for robustness) to a stop instant, assumingparallel edges or not and both measures are in distance and amplitude.

It is also possible to use a simplified angular decision based on thesign of the vertical time-of-arrival difference and a horizontaltime-of-arrival difference with the detection based on the sign anddifferences of their absolute values. A start instant, stop instant ormiddle instant may be used. This is an extension of a hard decisioncriterion that takes into account the individual time-of-arrivals oneach laser detector 34 and attempts to derive a soft or non-binarymeasure. Once normalized, the time-of-arrival in a first detector 34equals 0, and the time-of-arrival in the last detector equals 1. Thenormalized times-of-arrival usually comply with a profile and “0” forthe first triggered detector and 0.5 for the second and third triggereddetectors as neighbors to the first triggered detector and 1 for thelast triggered detector opposite to the first detector. Each gesture maybe associated with a specific temporal profile. It is possible tocompute the normalized temporal instants of a received signal andcompute the distances to the normalized temporal profiles for eachgesture. The detected gesture is the one with the smallest distance andthe start instants and middle instants may be used.

It is possible that different detectors 34 may give differentrecognition results, and an individual detector may be “good” in certaincircumstances and “bad” in others, while it may be the opposite case foranother detector. The idea is to combine the “strengths” of each laserdetector 34 and use a voting principle when several detectors arerunning parallel on the same signal. A voting process may be organizedamong all possible gesture candidates with one vote per laser detector34 and the gesture candidate which obtains the majority is selected asthe probable gesture.

The controller 36 will calculate the “strengths” of individual detectors34. The confusion matrix will give a good insight into the capability ofa detector 34 to provide the correct gesture, knowing which gesture hasbeen made.Confusion Matrix of detector d: C ^(d)(i,j)=P(

|G _(j))

(i: column index, j: row index)

The controller 36 may derive a likelihood matrix, which gives theprobability that a specific gesture has been made, knowing which gesturehas been detected.Likelihood Matrix of detector d: L ^(d)(i,j)=P(G _(i)|

)

The controller may assume an equal distribution of the gestures of thetest database:

L^(d)(i, j) ∝ P(❘G_(i))P(G_(i))${L^{d}\left( {i,j} \right)} = \frac{C^{d}\left( {j,i} \right)}{\sum\limits_{j}{C^{d}\left( {j,i} \right)}}$

The controller 36 may combine the results of several individualdetectors 34. Assuming that each detector d (from 1 to D) givesindependent results:

-   -           The controller obtains:        P(G _(i)|        , . . . ,        , . . . ,        )=P(G _(i)|        )× . . . ×P(G _(i)|        )× . . . ×P(G _(i)|        )/P(G _(i))^(D-1)

The optimal outcome of this combination of detectors 34 is the one whichmaximizes the previous a posteriori probability among all possiblevalues of i. Assuming an equally distributed gesture, it corresponds tomaximize its numerator.

For each detector d and for each possible detected value:

-   -           The vector of its corresponding likelihoods is associated:        (P(G ₁|        ), . . . ,P(G _(n)|        ))=L ^(d)(1,j _(d)), . . . ,L ^(d)(n,j _(d)))

i.e. the j_(d) ^(th) row of L^(d).

The controller 36 combines the results of several individual detectorsas follows:

a) By multiplying, term by term, each likelihood vector associated toindividual detector results, the controller obtains a combined vectorwhich represents:P(G _(i)|

, . . . ,

, . . . ,

),iε{1,n}

b) The result is:argmaxP(G _(i)|

, . . . ,

, . . . ,

),iε{1,n}

c) For implementation consideration, the controller may replace theterm-by-term product of likelihood vectors by the term-by-term sum oflog-likelihoods;

d) Compared to the naïve voting process, this optimal detectorcorresponds to assigning <<soft>> voting values (namely the likelihoods)during a voting process; and

e) These likelihoods are computed offline (based on the results ofindividual detector on the training samples) and need to be stored (16values per detector in this case).

The electronic device 30 as described with the laser source 32, laserdetector 34 and controller 36 exhibits a success rate comparable to muchmore complicated machine learning based detector systems and has lowcomputational complexity with only a few additions and fewermultiplications. There are no divisions and no trigonometric functionsand a limited memory requirement is required with a simple logic. Theprocessing is easily adapted to a microcontroller or other processors aspart of a small mobile wireless communications device. It is alsoadvantageous over other detector systems that use position detectionsystems and phase-based sensing.

This application is related to copending patent application entitled,“DEVICE AND METHOD FOR IDENTIFYING TAP OR WIPE HAND GESTURES USINGTIME-OF-FLIGHT SENSING,” which is filed on the same date and by the sameassignee and inventors, the disclosure which is hereby incorporated byreference.

Many modifications and other embodiments of the invention will come tothe mind of one skilled in the art having the benefit of the teachingspresented in the foregoing descriptions and the associated drawings.Therefore, it is understood that the invention is not to be limited tothe specific embodiments disclosed, and that modifications andembodiments are intended to be included within the scope of the appendedclaims.

That which is claimed is:
 1. An electronic device comprising: at leastone laser source configured to direct laser radiation toward a user'shand; a plurality of laser detectors configured to receive reflectedlaser radiation from the user's hand; and a controller coupled to the atleast one laser source and plurality of laser detectors and configuredto determine a set of distance values to the user's hand for eachrespective laser detector and based upon a time-of-flight of the laserradiation, and determine a hand gesture from among a plurality ofpossible hand gestures based upon the sets of distance values usingrespective Bayesian probabilities associated with each respective laserdetector.
 2. The device according to claim 1 wherein said controller isconfigured to derive the Bayesian probabilities as a confusion matrixfor each of the laser detectors.
 3. The device according to claim 1wherein said controller is configured to weight the sets of distancevalues using the Bayesian probabilities to determine the hand gesture.4. The device according to claim 1 further comprising a memory coupledto the controller and configured to store the sets of distance values.5. The device according to claim 1 wherein said controller is configuredto determine the hand gesture as at least one of a single tap, a doubletap, a page flip, a single wipe, a double wipe, and a rotation.
 6. Thedevice according to claim 1 wherein each laser detector comprises asingle photon avalanche diode (SPAD) detector.
 7. The device accordingto claim 6 wherein said SPAD detector comprises an array of singlephoton avalanche diodes.
 8. The device according to claim 6 wherein saidlaser source, SPAD detector, and controller are formed as a singleintegrated circuit (IC).
 9. The device according to claim 1 wherein saidlaser source comprises a vertical-cavity surface-emitting laser (VCSEL).10. The device according to claim 1 wherein said laser source comprisesan infrared (IR) laser source.
 11. An electronic device comprising: atleast one laser source configured to direct laser radiation toward auser's hand, said at least one laser source comprising a vertical-cavitysurface-emitting laser (VCSEL); a plurality of single photon avalanchediode (SPAD) detectors configured to receive reflected laser radiationfrom the user's hand; and a controller coupled to the at least one lasersource and plurality of SPAD detectors and configured to determine a setof distance values to the user's hand for each respective SPAD detectorand based upon a time-of-flight of the laser radiation, and determine ahand gesture from among a plurality of possible hand gestures based uponthe sets of distance values using Bayesian probabilities, eachrespective SPAD detector being associated with a respective Bayesianprobability.
 12. The device according to claim 11 wherein saidcontroller is configured to derive the Bayesian probabilities as aconfusion matrix for each of the laser detectors.
 13. The deviceaccording to claim 11 wherein said controller is configured to weightthe sets of distance values using the Bayesian probabilities todetermine the hand gesture.
 14. The device according to claim 11 furthercomprising a memory coupled to the controller and configured to storethe sets of distance values.
 15. The device according to claim 11wherein said controller is configured to determine the hand gesture asat least one of a single tap, a double tap, a page flip, a single wipe,a double wipe, and a rotation.
 16. The device according to claim 11wherein each SPAD detector comprises an array of single photon avalanchediodes.
 17. The device according to claim 11 wherein said laser source,SPAD detector, and controller are formed as a single integrated circuit(IC).
 18. The device according to claim 11 wherein said laser sourcecomprises an infrared (IR) laser source.
 19. A method of determining ahand gesture comprising: directing laser radiation from at least onelaser source toward a user's hand; receiving with a plurality of laserdetectors reflected laser radiation from the user's hand; and using acontroller coupled to the at least one laser source and plurality oflaser detectors to determine a set of distance values to the user's handfor each respective laser detector and based upon a time-of-flight ofthe laser radiation, and determine a hand gesture from among a pluralityof possible hand gestures based upon the sets of distance values usingBayesian probabilities, each respective laser detector being associatedwith a respective one of the Bayesian probabilities.
 20. The methodaccording to claim 19 further comprising deriving the Bayesianprobabilities as a confusion matrix for each of the laser detectors. 21.The method according to claim 19 further comprising weighting the set ofdistance values using the Bayesian probabilities to determine the handgesture.
 22. The method according to claim 19 further comprisingdetermining the hand gesture as at least one of a single tap, a doubletap, a page flip, a single wipe, a double wipe, and a rotation.
 23. Themethod according to claim 19 wherein each laser detector comprises asingle photon avalanche diode (SPAD) detector.
 24. The method accordingto claim 23 wherein the laser source, SPAD detector, and controller areformed as a single integrated circuit (IC).
 25. The method according toclaim 19 wherein the laser source comprises a vertical-cavitysurface-emitting laser (VCSEL).